Pulse Transit Time-Pulse Wave Analysis Fusion Based on Wearable Wrist Ballistocardiogram for Cuff-Less Blood Pressure Trend Tracking

Yousefian, Peyman; Shin, Sungtae; Mousavi, Azin; Tivay, Ali; Kim, Chang‐Sei; Mukkamala, Ramakrishna; Jang, Dae-Geun; Ko, Byung Hoon; Lee, Jongwook; Kwon, Ui-Kun; Kim, Youn Ho; Hahn, Jin-Oh

doi:10.1109/access.2020.3012384

Cited by 23 publications

(6 citation statements)

References 31 publications

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“…Novel methods replace the oscillometric measurements of BP by cuffless measurements often using photoplethysmography (PPG), ballistocardiography (BCG) and/or electrocardiography (ECG) to estimate BP. This is performed by either analysing the pulse wave of the PPG signal (pulse wave analysis, PWA) ( 7 ), as is the case in the smartwatch that was used in this study, or by analysing a combination of PPG and ECG or BCG to calculate the pulse transit time (PTT) ( 8 , 9 ). Other methods that are being investigated include the use of bio-impedance sensors for BP measurement ( 10 ).…”

Section: Introductionmentioning

confidence: 99%

Smartwatch-Based Blood Pressure Measurement Demonstrates Insufficient Accuracy

Falter

Scherrenberg

Driesen

et al. 2022

Front. Cardiovasc. Med.

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BackgroundNovel smartwatch-based cuffless blood pressure (BP) measuring devices are coming to market and receive FDA and CE labels. These devices are often insufficiently validated for clinical use. This study aims to investigate a recently CE-cleared smartwatch using cuffless BP measurement in a population with normotensive and hypertensive individuals scheduled for 24-h BP measurement.MethodsPatients that were scheduled for 24-h ambulatory blood pressure monitoring (ABPM) were recruited and received an additional Samsung Galaxy Watch Active 2 smartwatch for simultaneous BP measurement on their opposite arm. After calibration, patients were asked to measure as much as possible in a 24-h period. Manual activation of the smartwatch is necessary to measure the BP. Accuracy was calculated using sensitivity, specificity, positive and negative predictive values and ROC curves. Bland-Altman method and Taffé methods were used for bias and precision assessment. BP variability was calculated using average real variability, standard deviation and coefficient of variation.ResultsForty patients were included. Bland-Altman and Taffé methods demonstrated a proportional bias, in which low systolic BPs are overestimated, and high BPs are underestimated. Diastolic BPs were all overestimated, with increasing bias toward lower BPs. Sensitivity and specificity for detecting systolic and/or diastolic hypertension were 83 and 41%, respectively. ROC curves demonstrate an area under the curve (AUC) of 0.78 for systolic hypertension and of 0.93 for diastolic hypertension. BP variability was systematically higher in the ABPM measurements compared to the smartwatch measurements.ConclusionThis study demonstrates that the BP measurements by the Samsung Galaxy Watch Active 2 show a systematic bias toward a calibration point, overestimating low BPs and underestimating high BPs, when investigated in both normotensive and hypertensive patients. Standards for traditional non-invasive sphygmomanometers are not met, but these standards are not fully applicable to cuffless devices, emphasizing the urgent need for new standards for cuffless devices. The smartwatch-based BP measurement is not yet ready for clinical usage. Future studies are needed to further validate wearable devices, and also to demonstrate new possibilities of non-invasive, high-frequency BP monitoring.

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Section: Introductionmentioning

confidence: 99%

Smartwatch-Based Blood Pressure Measurement Demonstrates Insufficient Accuracy

Falter

Scherrenberg

Driesen

et al. 2022

Front. Cardiovasc. Med.

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“…The design of the wearable system has also gradually developed from a multi-device signal acquisition system [ 87 ] to a single-device signal acquisition system. Many modules such as the PPG and ECG sensors are integrated into the same device, such as smartphones [ 11 , 88 ] and smartwatches [ 89 , 90 , 91 ] (the most popular), to continuously improve the integration of wearable systems.…”

Section: Discussion and Outlookmentioning

confidence: 99%

Wearable Continuous Blood Pressure Monitoring Devices Based on Pulse Wave Transit Time and Pulse Arrival Time: A Review

Zhou

Cui

et al. 2023

Materials

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Continuous blood pressure (BP) monitoring is of great significance for the real-time monitoring and early prevention of cardiovascular diseases. Recently, wearable BP monitoring devices have made great progress in the development of daily BP monitoring because they adapt to long-term and high-comfort wear requirements. However, the research and development of wearable continuous BP monitoring devices still face great challenges such as obvious motion noise and slow dynamic response speeds. The pulse wave transit time method which is combined with photoplethysmography (PPG) waves and electrocardiogram (ECG) waves for continuous BP monitoring has received wide attention due to its advantages in terms of excellent dynamic response characteristics and high accuracy. Here, we review the recent state-of-art wearable continuous BP monitoring devices and related technology based on the pulse wave transit time; their measuring principles, design methods, preparation processes, and properties are analyzed in detail. In addition, the potential development directions and challenges of wearable continuous BP monitoring devices based on the pulse wave transit time method are discussed.

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“…A total of 46 features were extracted from the fiducial points derived from multiple physiological signals considered in this work [ 24 , 30 ] ( Figure 2 ): Heart rate and heart rate variability based on three different methods [ 31 ] were calculated, including (i) time-domain method HRV T , (ii) Poincare method HRV P , and (iii) frequency-domain method HRV F . These constituted 4 features (HRV T , HRV P , HRV F , and heart rate).…”

Section: Methodsmentioning

confidence: 99%

Classification of Blood Volume Decompensation State via Machine Learning Analysis of Multi-Modal Wearable-Compatible Physiological Signals

Chalumuri

Kimball

Mousavi

et al. 2022

Sensors

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This paper presents a novel computational algorithm to estimate blood volume decompensation state based on machine learning (ML) analysis of multi-modal wearable-compatible physiological signals. To the best of our knowledge, our algorithm may be the first of its kind which can not only discriminate normovolemia from hypovolemia but also classify hypovolemia into absolute hypovolemia and relative hypovolemia. We realized our blood volume classification algorithm by (i) extracting a multitude of features from multi-modal physiological signals including the electrocardiogram (ECG), the seismocardiogram (SCG), the ballistocardiogram (BCG), and the photoplethysmogram (PPG), (ii) constructing two ML classifiers using the features, one to classify normovolemia vs. hypovolemia and the other to classify hypovolemia into absolute hypovolemia and relative hypovolemia, and (iii) sequentially integrating the two to enable multi-class classification (normovolemia, absolute hypovolemia, and relative hypovolemia). We developed the blood volume decompensation state classification algorithm using the experimental data collected from six animals undergoing normovolemia, relative hypovolemia, and absolute hypovolemia challenges. Leave-one-subject-out analysis showed that our classification algorithm achieved an F1 score and accuracy of (i) 0.93 and 0.89 in classifying normovolemia vs. hypovolemia, (ii) 0.88 and 0.89 in classifying hypovolemia into absolute hypovolemia and relative hypovolemia, and (iii) 0.77 and 0.81 in classifying the overall blood volume decompensation state. The analysis of the features embedded in the ML classifiers indicated that many features are physiologically plausible, and that multi-modal SCG-BCG fusion may play an important role in achieving good blood volume classification efficacy. Our work may complement existing computational algorithms to estimate blood volume compensatory reserve as a potential decision-support tool to provide guidance on context-sensitive hypovolemia therapeutic strategy.

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Pulse Transit Time-Pulse Wave Analysis Fusion Based on Wearable Wrist Ballistocardiogram for Cuff-Less Blood Pressure Trend Tracking

Cited by 23 publications

References 31 publications

Smartwatch-Based Blood Pressure Measurement Demonstrates Insufficient Accuracy

Smartwatch-Based Blood Pressure Measurement Demonstrates Insufficient Accuracy

Wearable Continuous Blood Pressure Monitoring Devices Based on Pulse Wave Transit Time and Pulse Arrival Time: A Review

Classification of Blood Volume Decompensation State via Machine Learning Analysis of Multi-Modal Wearable-Compatible Physiological Signals

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